Due to its spoken nature, Europarl contains frequent sentence-initial forms of address.

left adjacent elements that are not integrated parts of the sentence as in example (4).


A number of authors have provided polynomial time algorithms for parsing with CCG which gives some insight into how good our bound of O(n 5 ) is.

vijay-Shanker and Weir (1994) provided a chart parsing algorithm for CCG with a time bound of O(n 6 ).


Both expressions are grammatical This paper proposes a probabilistic model for computing how likely a given pair of expressions satisfy the aforementioned three conditions.

we focus on the post-generation assessment of automatically generated productive paraphrases of predicate phrases in Japanese.


This paper studies sentiment analysis from the user-generated content on the Web.

it focuses on mining opinions from comparative sentences, i.e., to determine which entities in a comparison are preferred by its author.


For instance, to associate transitive verbs with a binary relation schema, the syntactic speci-fication given in (1) is modified to: Next the correct syntax/semantic interface constraints must be specified for each diathesis.

the correct mapping between syntactic and semantic arguments must be enforced.


In this paper, we present an approach to the classification of named entities into fine-grained ontological categories based on a method successfully employed in lexical substitution.

1 we predict the fine-grained category of a named entity, previously recognized, by simply estimating the plausibility of sentences where the entity to be classified is substituted with the ones contained in the training data, in our case, a partially populated ontology.


We evaluated our method on the benchmark proposed by Tanev and Magnini (2006) to provide a fair comparison with other approaches, and on a general purpose ontology of people derived from WordNet (Fellbaum, 1998) to perform a more extensive evaluation.

the experiments were designed to investigate the effectiveness of our approach at different levels of generality and with different amounts of training data.


To estimate the correctness of each substitution, we calculate a plausibility score using a modified version of the lexical substitution algorithm introduced in , that assigns higher scores to the substitutions that generate highly frequent sentences on the Web.

this technique ranks a given list of synonyms according to a similarity metric based on the occurrences in the Web 1T 5-gram corpus, 2 which specify n-grams frequencies in a large Web sample.


), where the category distinctions are clearer, while a further fine-grained classification, in some cases, is even difficult for human annotators.

results are higher for finegrained categories densely populated and with a small number of sibling categories (i.e., Painter and Musician).


This is because we cannot decide how many words there will be in a document and how many characters there will be in a certain word in advance.

the structure of the ideal model is not fixed.


Why do we sample z i , z i+1 , …, z i+l-1 together?

assume that we draw z i , z i+1 , …, z i+l-1 one by one, and it is now time to sample z i+1 according to the conditional probability ( ) Recall step 6-b in the generative TWC model: it says " as this probability is a factorial of the target probability, it follows that ( ) z i+1 should be equal to z i and not change during sampling.


We also found that both perplexity curves increased with the number of partitions.

both models suffer from overfitting issues.


MI can capture the associations between words, whether they are adjacent or not.

are estimated using Maximum Likelihood Estimation on the same training data as for training PTM.


To measure this kind of structural similarity, we develop a measure function called MI-based structural similarity score.

ij is the mutual information of f i and f j (i.e., in formula (6)).


In this paper, we study the influence of syntactic representation on the performance of SRL systems.

we compare constituent-based and dependencybased representations for SRL of English in the FrameNet paradigm.


In the mean field approximation, p(z|n) is approximated by a factorized distribution q(z), in which all hidden variables are independent: Using q(z) instead of p(z|n), computation of the E-step can be written as follows: The parameters of q(z) are determined such that the KL divergence between q(z) and p(z|n) is minimized.

the approximate distribution q(z) is determined by minimizing under the condition that k q i (z i = k) = 1 for all i.


For example, by applying the patterns (b) and (c), the pattern (a) can then be applied to the expressions "N " ('I think that I want N') and "N " ('Though I want N, I don't have enough money'), respectively, even though "N * --" doesn't appear at the top of the trees.

the auxiliary patterns contribute to generate variations of the demand patterns.


This paper presents a contribution to the work on relation extraction by combining statistical information with string distance measures.

we propose to use a local alignment kernel to detect relations.


In our view, they are the most natural measures for the text data.

if we are to compare any two words given two sequences of words, the elements sharing the same contexts should be more similar to each other than those that do not.


To compute LA matrices we use the distributed ASCI supercomputer 3 (DAS-3) 4 which allows us to speed up the process of sequence comparison.

because of symmetricity of the resulting matrices for n sequences we need to carry out n(n−1)/2 comparisons to build a matrix.


Among them, the best parsers are the SVM-based dependency analyzers (Kudo and Matsumoto, 2002;Sassano, 2004).

sassano added some features to improve his parser by enabling it to detect coordinate structures (sassano, 2004).


In our model, both of the pre-conjunct and post-conjunct are generated from the predicate.

in (b), both ane (sister) and otouto (brother) with wo (ACC) are generated from yonda (invited).


Another cause of the low accuracy is the out-of-domain training corpus.

the parser is trained on a newspaper corpus, whereas the test corpus is obtained from the web, because of the non-availability of a tagged web corpus that is large enough to train a supervised parser.


For this purpose, we propose to use a set of transformation rules that allow us to find a set of formulae logically equivalent to the original formula; the aim is to make the set large enough that all the relevant expressive choices (as investigated in this paper) are represented.

we need the following rules that operate on dnfs (where A is an adjective; B 1 and B 2 are nouns; X and Y are combinations of adjectives and nouns).


The acquisition and classification of such region-specific lexical items is an important step toward the larger goal of constructing a Pan-Chinese lexical resource.

we extend a previous study in three respects: (1) to improve automatic classification by removing duplicated words from the thesaurus, (2) to experiment with classifying words at the subclass level and semantic head level, and (3) to further investigate the possible effects of data heterogeneity between the region-specific words and words in the thesaurus on classification performance.


Size   Kwong and Tsou (2006) observed that among the unique lexical items found from the individual subcorpora, only about 30-40% are covered by Cilin, but not necessarily in the expected senses.

cilin could in fact be enriched with over 60% of the unique items from various regions.


Our task is related to but distinct from sentiment analysis, which focuses on judgments in opinions and, recently, predictions given by opinions.

kim and Hovy (2007) identify which political candidate is predicted to win by an opinion posted on a message board and aggregate opinions to correctly predict an election result.


Finally, the time information of the nodes and the edges were used to modify the random walk model as was used in PageRank.

they used it to modify the random jump probabilities (in both T-Rank Light and T-Rank) and the transition probabilities (in T-Rank only).


This is because they directly applied the original PageRank to rank the pages.

the old pages still obtained higher scores while the newly coming pages still got lower scores.


The nodes and edges can not be deleted or modified once they are inserted.

we are only interested in the changes caused when new sentences are introduced into the existing text graph.


The differences are two-fold: (1) the document collection(s) used to build the text graph are different; and (2) after ranking, the sentence selection strategies are different.

pR(B) only uses the sentences in "B" to build the graph, and the other two consider the sentences in both "A" and in "B".


In this work, we use the sentences in the same sentence set for positive reinforcement and sentences in the different set for negative reinforcement.

the old sentences perform negative reinforcement over the new sentences while the new sentences perform positive reinforcement over each other.


For phrases containing multiple very frequent subphrases, we precompute the list of occurrences into an inverted index.

if both u and v are frequent, we simply precompute all locations of uXv and vXu.


For phrases pairing a frequent subphrase with infrequent subphrases, we use a merge algorithm whose upper bound complexity is logarithmic in the number of occurrences of the frequent subphrase.

if count(u) is small, and count(v) is big, then we can find uXv in at most O(count(u) • log(count(v))) time.


We propose another method for the crossframework performance analysis of the parsers wherein the output of parsers are first converted to a CFG tree.

we use CFG trees of the style used in the Penn Treebank (PTB) (Marcus et al., 1994), in which the non-terminal labels are simple phrasal categories (i.e., we do not use function-tags, empty nodes, and co-indexing).


The synchronous productions were obtained by splitting the tree pairs at several pairs of non-terminal nodes.

for a tree pair (T 1 , T 2 ) for a sentence s, 1. we select a set of 'common-span node pairs' .


The two synchronous grammars were created by applying different pre-processing to the parallel treebank.

for the HPSG→PTB-CFG direction, 1) the PTB-CFG trees in the parallel treebank were binarized in such a way that maximized the number of commonspan node pairs; 2 2) commas in the PTB-CFG trees were raised as high as possible, approximating the change of the position of commas by the Mayz program; 3) the POS tags for 'not' are changed from 'RB' to 'RB-not,' because in PTB, 'not' is treated differently from other adverbs; 4) base NPs in PTB-CFG trees are marked as 'NP-B.'


To closely examine the difference in the parsers' performances, we conducted a comparative error analysis using the word-word dependency representation of the parsing results.

we tested the difference in the ability of resolving specific types of syntactic ambiguities between two parsers by using McNemar's test.


8 Past 7-day Window (inclusive): This baseline looks for the specified day in a 7-day window whose last day is the temporal focus.

day names are always assumed to refer to days in the last week, including the 'day of speaking'.


Although these are precisely the kinds of texts where, in our own work, we need to provide fast processing of large volumes of text, it remains to be seen how these heuristics fare when faced with a broader range of text types.

other text types are likely to require more sophisticated approaches to temporal focus tracking than we have used here.


If the probability is non-zero, we add the parse to the n-best list W , otherwise we ignore the parse.

we find parses that the WSJ trained model could have produced but didn't due to search heuristics.


Our goal is to find out which features, if any, can predict these fscore differences.

we ask the question of whether seeing more unknown items indicates whether we are more likely to see improvements when self-training.


A positive change means that performance improved under self-training.

looking at Figure 1a, the greatest performance improvement occurs, perhaps surprisingly, when we have seen no unknown words.


Recall that APPROACH is the component that assesses language and communication skills.

it assesses the style and appropriateness of the way candidates convey information, from lexical choice to displaying empathy through communication style.


Therefore, for any given inventory, we can define the binary entropy H f (Shannon and Weaver, 1949) for the feature f as If F is the set of all features present in the consonants forming the inventory, then feature entropy F E is the sum of the binary entropies with respect to all the features, that is (3) Since we have assumed that f is an independent random variable, F E is the joint entropy of the system.

f E provides an estimate of the number of discriminative features present in the consonants of an inventory that a speaker (e.g., parent) has to communicate to a learner (e.g., child) during language transmission.


In order to circumvent the above the ordinate pairs (say y and y ) where the abscissas are equal.

if there are N such ordinate pairs then the mean error can be expressed as problem, we introduce the concept of triad (i.e., fully connected triplet) formation and thereby refine the model in the following section.


The LDA model assumes the bag-of-words model and posits that each document is composed of different topics.

each word's existence is attributed to one of the document's topic.


More recently, much research in summarisation has focused on the use of phrasal concepts such as events to represent sentences in extractive summarisation systems.

(Filatova and Hatzivassiloglou, 2004) use eventbased features to represent sentences and shows that this approach improves the quality of the final summaries when compared with a baseline bag-ofwords approach.


Overall these results suggest that the SVM using IG for feature selection is the most effective method for correctly classifying both "onevent" and "off-event" sentences.

it achieves 90.23% and 96.70% F1 score for these Table 4: % Precision, Recall and F1 for both classes as well as the classification accuracy achieved by all algorithms using a 50:50 train/test split where the target event type is "Die".


Another interesting contribution of this paper is that the trigger-based classification baseline performed better than expected.

for three of the six event types it out-performed the trained SVM.


Note that only four major types out of the nine types of anaphoric pronouns mentioned in the previous section are counted.

the chosen types correspond to those rows in Table 2 that contain at least two O.


The number property of these mentions is characterized in the markable detection phase based on the part-of-speech tag, the head noun, and the phrase structure of those mentions.

the MUC corpus contains many coordinated-structured mentions (Section 2.1), which are difficult for markable characterization.


The kernel functions used in support vector machine classifiers also allow an interpretation as similarity measures; however, not all similarity measures can be used as kernels.

kernel functions must satisfy the mathematical property of positive semi-definiteness.


To measure distributional similarity, we use a representation of words based on observation of their relations with other words.

a target word w is represented in terms of a set C of admissible co-occurrence types c = (r, w ), where the word w belongs to a co-occurrence vocabulary V c and r is a relation that holds between w and w .


A negative semi-definite (nsd) kernel is a symmetric functionk : X × X → R such that for all finite n-element sets X ⊆ X and for all vectors (2) Whereas positive semi-definite kernels correspond to inner products in a Hilbert space F, negative semi-definite kernels correspond to squared distances.

ifk(x, y) = 0 only when x = y then k is a metric.


We define a (x) and a (y) in order to represent the first and second elements of the letter mapping a.

a (x) and a (y) are equal to i and j respectively, when a = (i, j).


Inspired by feature engineering for Conditional Random Fields (CRFs) (Lafferty et al., 2001), we design two kinds of features: unigram (state) features defined on each letter mapping, and bigram (transition) features defined on each pair of adjacent letter mappings.

given a triplet, a, x, and y, a global feature function f k (a, x, y) ∈ F sums up the boolean values (0 or 1) of the corresponding local feature ( 3)f k (a, x, y) counts the number of times the local feature is fired in the alignment a.


Using a simple measure of association between properties and queries, explained in detail later in this paper, one can rank each property and obtain the following three highest scoring properties for explaining the similarity of these terms: {they are products, they can be imported, they can be exported} Even though can be imported and can be exported are highly ranked explanations, taken jointly, once we know one the other does not offer much more information since most things that can be imported can also be exported.

there is a large overlap in information between the two properties.


Although we expect our joint information model, presented in Section 4.2, to generalize to many ranking tasks, our focus in this paper is on the task of choosing the n-best explanations that describe the similarity of a set of terms.

given a set of terms, one must choose the best set of characterizations of why the terms are similar, chosen from a large set of possible explanations.


The set of properties: {they are products, they can be imported, they can be eaten} is preferred over the set {they are products, they can be imported, they can be exported} since it reduces more the uncertainty of what the original query is.

if we hid the query {apple, ume, pawpaw, quince} from a person, the first set of properties would help more that person guess the query elements than the second properties.


Each cluster consists of a set of articles, in which the topic phrase is matched within the title or the content of papers in AAN.

the five clusters that we collected this way, are: Dependency Parsing (DP), Phrased Based Machine Translation (PBMT), Text Summarization (Summ), Question Answering (QA), and Textual Entailment (TE).


The similarity that we use, as described in section 3.2, is such that sentences with the same facts have high similarity values.

strong edges in the citation summary network are likely to indicate a shared fact between two sentences.


• Cluster Lexrank (C-lexrank) The second method we used was Lexrank (Erkan and Radev, 2004) inside each cluster.

for each cluster Ω i we made a lexical network of the sentences in that cluster (N i ) .


Based on the described evaluation method we conducted a number of experiments to evaluate different summaries of a given length.

we use a gold standard and a random summary to determine how good a system summary is.


As indicated as follows, each CFG rule has the form: P L n …L 1 H R 1 …R m Here, P is the parent node, H is the head child of the rule, L n …L 1 and R 1 …R m are left and right modifiers of H respectively, and both n and m may be zero.

the parent node P depends on the head child H, this is what we call constituent dependency.


For every right hand side, the NP in bold is modified by the constituent following them.

the latter is dependent on the former, and may be reduced to a single NP.


If an unknown word such as 用电 yongdian (electricity used) is tested, it would be analyzed as 用/V 电 /N (to use, electricity) for the feature extraction and then it would be tagged as 用/V/N_B 电 /N/N_E.


In fact, our initial model based on the CRFs model has learned the structure rules of Chinese words and can even give a credibility score for each rule.

most of the rules have been incorporated by the utilization of the CRFs model.


Before moving on to an empirical investigation of constraining context-free parsers with the methods we propose, we first need to take a fairly detailed look at representations internal to these parsers.

while we can rule out multi-word constituents with particular start and end positions, there may be intermediate or incomplete structures within the parser that should not be ruled out at these same start and end positions.


Since it is costly and time-consuming to create a large-scale training corpus annotated by biologists, we need to concede to use coarse-grained biological NE categories.

the NER component is trained on the five NE super classes, i.e., Protein, DNA, Experimental, Organisms, and Processes.


The NER models are trained by CRFs (Lafferty et al., 2001) using the standard IOB2 labelling method.

the label ``B-NE'' is given to the first token of the target NE sequence, "I-NE" to each remaining token in the target sequence, and ``O'' to other tokens.


Then, the semantic role labelling models are trained by CRFs in a similar way to NER.

the label ``B-Role'' is given to the first token of the target Role sequence, "I-Role" to each remaining token in the target sequence, and "O" to other tokens.


These requirements will be minimized by recognizing trees in a rightcorner form, which accounts partially recognized phrases and clauses as incomplete constituents, lacking one instance of another constituent yet to come.

this study will use the trees in the Penn Treebank Wall Street Journal (WSJ) corpus (Marcus et al., 1994) as a data set.


The first approach is a simple frequency cutoff, applied as a pre-processing to filter out words and contexts with low frequency and to reduce computational cost.

all words w such that c N (w, c) < θ f and contexts c such that w N (w, c) < θ f , with θ f = 5, are removed from the co-occurrence data.


This paper focuses on such relations between the targets of opinions in discourse.

in this work, we propose a scheme of opinion frames, which consist of two opinions that are related by virtue of having united or opposed targets.


We believe that our opinion frames, anaphoric relations and discourse relations can symbiotically help disambiguate each other in the discourse.

suppose that some aspect of an individual opinion, such as polarity, is unclear.


Their model relies on semantic frames and extracts as the topic the syntactic constituent at a specific argument position for the given verb or adjective.

kim and Hovy extract what we refer to as the target spans, and do so for a subset of the opinion-bearing words in the text.


With the above examples and problems in mind, we hypothesize that the notion of topic coreference will facilitate both the manual and automatic identification of opinion topics: We say that two opinions are topic-coreferent if they share the same opinion topic.

we conjecture that judging whether or not two opinions are topic-coreferent is easier than specifying the topic of each opinion (due to the problems described above).


This paper uses insights from generative linguistics to guide the solution of locality of arguments.

maximal Projection (mP) which dominates 1 active region of arguments according to the projection principle of principle and parameters.


MP holds all lexical properties of heads.

the MP of a predicate holds predicate structure information and the constituents out of its domain cannot occupy argument positions.


In contrast, for "He gave her -", the phrase started by 'her' is normally selfcomplete, and makes the next label more likely to be B, e.g., "[He] gave [her] [flowers]".

latent-dynamics is an intermediate representation between input features and labels, and explicitly modeling this can simplify the problem.


In other words, latent-dynamics is an intermediate representation between input features and labels, and explicitly modeling this can simplify the problem.

in many real-world cases, when the part-of-speech tags are not available, the modeling on latent-dynamics would be particularly important.


In the experiments reported in this section, we did not use the features on POS tags in order to isolate the model's capability of learning latent dynamics.

the model could simply learn the dynamics of POS tags as the latent dynamics if the model is given the information about POS tags.


On the other side, all the LDCRF models outperform the CRF model.

the gap between LDCRF-BLP and CRF is 1.53 percent.


On the other hand, Table 8 indicates that the three-class problem is much harder than the twoclass problem.

positive sentences tend to be classified as neutral.


The results also showed that our twostep emotion classification was effective for finegrained emotion classification.

fatal errors were significantly reduced with sentiment polarity classification before fine-grained emotion classification.


Figure (3) demonstrates how the realization phase models the correlation between grammatical relations and morphological information percolated from dominated surface forms.

our model can capture the interaction between marked features, e.g., the 'exclusive or' relation between definiteness and accusativity in marking direct objects in MH (Danon, 2001).


The difficulty is that the labels of the choice pairs must be hidden from the learning algorithm.

the training set consists of one positive example (the stem pair) and the testing set consists of five unlabeled examples (the five choice pairs).


We believe that most of our lexical knowledge is relational, not attributional.

meaning is largely about relations among words, rather than properties of individual words, considered in isolation.


On the other hand, methods that capture semantic dimensions are known to be useful in disambiguating different senses of a word.

pROBABILISTIC LATENT SEMANTIC ANAL-YSIS (pLSA) is known to simultaneously encode various senses of words according to latent semantic dimensions (Hofmann, 1999).


For example, consider the connections needed to make and understand the above metaphors: Burqa ⇒ ≡ for concealing a Muslim woman ≈ for protecting a Muslim woman ≈ protecting a woman ≈ for protecting a person Armor ⇐ Make-up ⇒ ≡ typically worn by women ≈ expected to be worn by women ≈ must be worn by women ≈ must be worn by Muslim women Burqa ⇐ In each case we see how metaphor draws out and highlights, in a modified or exaggerated form, an existing aspect of each target concept.

metaphor does not indiscriminately transplant arbitrary aspects of a source concept onto a target, but accommodates a selective graft of the most salient aspects of this source concept onto those aspects of the target that can be highlighted by the comparison (Ortony, 1979).


The hypothesis we wish to test is that these cues are sufficiently general to work as a classification device on a corpus from a different domain, even containing interactions of a different conversational style.

sWITCHBOARD is an open domain spoken human-human conversational corpus and we have shown state-of-the-art tagging performance over this data using our cuebased model.


After feature vectors of sentences are examined, a supervised learning classifier is then employed.

considering the length of final summaries is fixed, candidate sentences are re-ranked.


The number of such specialized synsets in the outer regions of the wordnet outweighs the number of core concepts by several orders of magnitude.

the majority of synsets in a wordnet is part of tree structures that are arranged around a relatively small core graph.


We want to augment BTG's formal structures with linguistic structures since they are both hierarchical.

our goal is to learn a more linguistically meaningful BTG from real-world bitexts by projecting linguistic structures onto BTG formal structures.


Instead of looking for features associated with terms as in other works, this paper looks for features associated with term delimiters.


It is important to point out that nly the stop y domain specific knowledge performs better than TV_ConSem using a large domain lexicon.

delimiter based extraction with link analysis use much less resources and still improve performance of TV_ConSem.


The performance of d radical -close to our random baseline, despite performing best in the previous task -is suggestive of the different characteristics of this task.

a metric which orders well across the broad spectrum of similarity pairs may not be well suited to identifying highsimilarity pairs, and vice-versa.


Experiments have also been conducted to compare the performance of the HVS model and the hy-  brid framework without employing the modified K-means clustering algorithm to automatically select the most representative sentences for annotation.

the whole corpus of 12702 sentences were provided with abstract annotations.


The current classifier considers such unlabeled examples near decision boundaries as the most informative examples in uncertainty sampling scheme for active learning.

we assume that an unlabeled example with maximum uncertainty has the highest chance to change the decision boundaries.


To solve this problem, we make an assumption that labeling an unlabeled example may shift the decision boundaries if this example was previously "outside" and is now "inside".

if an unlabeled example is automatically assigned to two different labels during two recent learning cycles 2 , we think that the labeling of this unlabeled example has a good chance to change the decision boundaries.


To summarize the SBC-based initial training set generation algorithm, let U={U 1 , U 2 , …, U N } be the set of unlabeled examples to be clustered, and k be the predefined size of initial training data set.

sBC technique selects k most representative unlabeled examples from U to generate the initial training data set.


We know both translations are ultimately generated from the same input, which makes our both candidates very close.

in (Mutton et al., 2007) the training data used human sentences as positive exemplars and very simple bigram-generated sentences as negative ones, so that there was a big difference in quality between them.


We give this estimate a total weight of one observation.

we set From a practical perspective, this can be interpreted as a smoothing when sample sizes are small, which is often the case for p(x|y).


10 The smaller P (matches = m) is, the less likely it is that we obtain m matches by pure chance.

if P (matches = m) is very small, m matches are more than we would expect to occur by pure chance.


An additional advantage of MCST-SVM classifiers over the other schemes is that MCST-SVM requires only k − 1 decision nodes for a k-class problem (and a maximum of k − 1 decisions to make a prediction).

only k − 1 SVMs must be trained, thereby reducing training time.


The Tanimoto coefficient provides a greater benefit for the four-class dataset.

when feature culling is used (Columns 4 and 5 in Table 1), accuracy improves by 2.63% and 2.27% for Authors A and B respectively (statistically significant), and by 1.29% and 0.70% for Authors C and D respectively.


Generally, the MCST-SVM is competitive against all the classifiers presented in (Pang and Lee, 2005), and in some cases significantly outperforms these methods.

the hierar-  chical classifier outperforms OVA+PSP by 7% in the three-class case for Author A (statistically significant), while in the four-class case the MCST-SVM outperforms the best competing methods by 7.72%, 3.89% and 4.98% for Authors A, B, and C respectively (statistically significant).


In cases when one system was judged to be fluent and the other was not, LMBR+CC was preferred about twice as often as the ML baseline (26.9% to 9.7%).

the monolingual fluency constraints were judged  to have improved the fluency of the low confidence region more than twice as often as a fluent hypothesis was made disfluent.


In this study, for simplicity, we assume that all word occurrences at a given distance count equally in the context model.

we ignore other features such as POS-tags, which are used in other studies on WSD.


We randomly split this set into two sets A and B.

• We want to find α that maximizes the similarity of the models obtained from the two sets, by minimizing their mutual cross entropy: we want α i to represent how much an occurrence at distance i models the context better than the collection language model, whose counts are weighted by the Dirichlet parameter.


Normally these approaches applied a clustering algorithm to obtain the theme clusters first and then ranked the sentences within each cluster or by exploring the interaction between sentences and obtained clusters (referring to Figure 1(b)).

clustering and ranking are regarded as two independent processes in these approaches although the cluster-level information has been incorporated into the sentence ranking process.


However, we show that by considering the relationship between our relations of interest, as well as how they relate to some existing knowledge resources, we improve the performance of RE.

the contributions of this paper are the following: • When our relations of interest are clustered or organized in a hierarchical ontology, we show how to use this information to improve performance.


However, we note that in that work, the authors performed their evaluation using undirected coarse-grained relations.

they do not distinguish on argument order of mentions and the classifier has to decide among 8 relation labels (7 coarse-grained relation types and a null label).


Secondly, prior work calculate their performance on relation extraction at the level of mentions.

each mention pair extracted is scored individually.


Thus, we evaluate our performance at the entity-level.

7 given a pair of entities, we establish the set of relation types existing between them, based on their mention annotations.


If r i,j > 0, we say that units u i and u j are positively correlated.

u i and u j tend to co-occur in the blocks; otherwise, both tend to be jointly-absent.


PCA calculates r i,j by using the correlation coefficient, whereas the other approaches employ the inner product or cosine formula 2 (Manning et al., 2008) to derive the relationship between textual units.

the correlation coefficient is identical to the cosine formula if we normalize each unit with its mean: and are the mean-normalized vectors of u i and u j , respectively.


We select the first k frequent person names, whose accumulated term frequencies reach 60% of the total frequencies, for evaluation.

the evaluated person names account for 60% of the person name occurrences in the examined blocks.


However, if the emotion cause is known in addition to the type of emotion, prediction of future events or assessment of potential implications can be done more reliably.

when emotion is treated as an event, causal relation is the pivotal relation to discover.


However, a cause can also be expressed as a nominal.

both the predicate and the two arguments are optional provided that at least one of them is present.


Differing from the existing cause systems, we formalize emotion cause detection as a multilabel problem.

given an emotion keyword and its context, its label is the locations of its causes, such as "left_1, left_0".


This converts multi-label classification to single-label classification, which is suitable for any multiclass classification technologies.

we choose a Max Entropy tool, Mallet 1 , to perform the classification.


As seen in Table 3, our system outperforms the existing approaches in almost all pairs of corpora.

in the multiple corporabased evaluations aimed at AIMed which has been frequently used as a standard set in PPI extraction, our approach shows prominent results compared with others.


If we could create a robustness rule that would absorb the object ('the marathon') without assigning a dependency, it would at least be able to identify the modifier dependency between 'ran' and 'yesterday'.

a fragment analysis solely combines items at the top level, whereas a robust parser would ideally be able to overcome barriers in both the lower and the higher regions of the chart, meaning that the damage can be localised and thus minimised.


This paper investigates the impact of using different temporal algebras for learning temporal relations between events.

we compare three intervalbased algebras: Allen (1983) algebra, Bruce (1972) algebra, and the algebra derived from the TempEval-07 campaign.


Previous work do not attempt to directly predict globally coherent temporal graphs, but instead focus on the the simpler problem of labeling pre-selected pairs of events (i.e., a task that directly lends itself to the use of standard classification techniques).

they do not consider the problem of linking pairs of events (i.e., of determining which pairs of events are related).


This paper adopts a more systematic perspective and directly assesses the impact of different relation sets (and their underlying algebras) in terms of learning and inferential properties.

we compare three interval-based algebras for building classification-based systems, namely: Allen (1983)'s 13 relation algebra, Bruce (1972)'s 7 relations algebra, and the algebra underlying Tempeval-07 3 relations (henceforth, TempEval algebra).


Nowadays, the given practice is to compute some sort of transitive closure over the network of constraints on temporal events (usually expressed in the well-studied Allen algebra (Allen, 1983)), and compute agreements over the saturated structures.

we can compare the sets of simple temporal relations that are deduced from it (henceforth, the "strict" metric), or measure the agreement between the whole graphs, including disjunctions (Verhagen et al., 2007) (henceforth, the "relaxed" metric).


5 For obvious sparsity issues, we did not include classes corresponding to disjunctive relations, as there are 2 |R| possible disjunctions for each relation set R. For training our models, we experiment with 4 various configurations that correspond to ways of expanding the set of training examples.

these configurations vary in: (i) whether or not we added the additional "Bethard relations" to the initial OTC annotations (Bethard et al., 2007), (ii) whether or not we applied saturation over the set of annotated relations.


Without resolving them, we lose opinions.

although we know that the second and fourth sentences express opinions, we do not know on what.


However, this PMI value cannot be encoded directly as a feature as it only captures the local information between antecedent candidates and opinion words.

it cannot be used as a global feature in the classifier.


This paper investigated the coreference resolution problem in the opinion mining context.

it studied object and attribute resolutions which are crucial for improving opinion mining results.


They are posting time, account authority, topic popularity and content relevance.

twitter maintains a specialized search engine which ranks tweets according to posting time and topic popularity.


In order to ensure the annotation is reasonable, we set multiple search intentions for each query referring to the topics arising in the tweets about the query in the corpus.

for Locations, tweets describing news related to the location are relevant.


The results of experiments demonstrate that the system using Sum_mention, First_list, Important_follower, length and URL performs best.

whether a tweet contains a URL is the most effective feature.


Bayesian Model Averaging (BMA) (Hoeting et al., 1999) is a technique designed to solve uncertainty inherent in model selection.

for SMT tasks, is a source sentence, is the training data, is the th SMT model trained on , represents the probability score predicted by that can be translated into a target sentence .


We expect to integrate the advantages of different SMT models into MBR decoding for further improvements.

we make in-depth investigation into MBR decoding concentrating on the translation distribution by leveraging a mixture model based on multiple SMT systems.


In this paper we are going to question this well-established belief and argue that just like in some cases syntax helps to find important content (e.g., when the input is an isolated sentence), in the multi-sentence case redundancy provides a reliable way of generating grammatical sentences.

the important and novel points of our work are as follows: • We present a simple and robust word graphbased method of generating succinct compressions which requires as little as a part of speech tagger and a list of stopwords.


As a first baseline we are searching for the most probable string with respect to the sentence cluster.

we use the Viterbi algorithm to find the sequence of words of a predefined length n which maximizes the bigram probability (MLEbased): Similar to the shortest path implementation, we specify compression length and set it also here to eight tokens.


This leads to 66.3 % precision (15.3% improvement) even for most difficult target type, PA. Again, when we look at Table 5, reasons 2-5 (33.3 %) will be improved.

"hypernym leads to ambiguity" makes up more than 10%.


We use the positional information of a node to determine if it is a VSN.

we check if Average(P ID vq ) ≤ σ vsn , where σ vsn is a parameter to be empirically set.


We use the natural ending points in the text to be summarized as hints to which node may be a valid end point of a path (i.e., a sentence).

a node is a valid end node if (1) the node is a punctuation such as period and comma or (2) the node is any coordinating conjunction (e.g., 'but' and 'yet').


The n-gram LMs are word-based backoff models, where the n-gram probabilities are estimated using Maximum Likelihood Estimation with smoothing.

for a trigram model, the smoothed probability is computed as where is the count of the n-gram in the training corpus and is a normalization factor.


The results show the benefits of capturing context information in error models.

the significant improvements resulting from PBEM demonstrate that the dependencies between words are far more effective than that between characters (within a word) for spelling correction.


As (Carroll and Oepen, 2005) have shown, packing and sharing are important factors in improving efficiency.

they show that an algorithm with packing and sharing clearly outtperforms the same algorithm without packing and sharing giving an up to 50 times speed-up for inputs with large numbers of realizations.


• Initial Search Space (ISS) : the number of all possible combinations of elementary trees to be explored given the result of lexical selection on the input semantics.

the product of the number of FTAG elementary trees selected by each literal in the input semantics.


A first observation is that RTGEN-all outperforms GENI in terms of intermediate structures built .

the Earley sharing and packing strategy is more effective in reducing the number of constituents built than the filtering and substitution-before-adjunction optimisations used by GENI.


The features used in RTGEN-selective mode are wh, xp, assign-comp, mode, definite, inv, assign-case, rel-clause, extracted and phon, in addition to the categorial and semantic information.

using all 42 SEMXTAG grammar features has the same impact on search space pruning as using only a small subset of them.


Future work will concentrate on further investigating the interplay in surface realisation between phrase structure and feature structure constraints.

(Maxwell and Kaplan, 1994) shows that a more sophisticated approach to constraint solving and to its interaction with chart processing renders the non interleaved approach more effective than the interleaved one.


We are not the first to attempt this task.

xia and McCord (2004) proposed a way to automatically learn reordering patterns for French-English.


If we limit our-Algorithm 1 Optimizing alignment links input: A set of aligned sentence pairs base = <empty sequence>; for several iterations do candidate rules = GenerateAllCandidateRules(input, base); base.append(MinCost(candidate rules)) end for selves to 3 children at a time we would attempt to match this node twice: with its children 1,2,3 and 2,3,4.

we pretend to consider two nodes, one with the first set of children, and one with the second, proceeding left to right.


However, when grammatical relations like subject and object are evaluated, parsing performance drops considerably (Green et al., 2009).

the decision to represent arguments in verbinitial clauses as VP internal makes VSO and VOS configurations difficult to distinguish.


In this paper, we introduce a new unsupervised methodology for synonym detection by extracting and aligning paraphrases on normalized domain corpora 1 .

we study a specific structure within aligned paraphrases, paraphrase casts, from which valid synonyms are discovered.


in Equations 4 and 5 which prioritize the alignment of specific domain key terms i.e., single match, or key expressions i.e., phrasal match, (reward 50), as well as lexically similar 5 words such as "programme" and "programming" for example.

for these similar words an adaptation of the well known Edit Distance is used, the c(., .)


For the purpose of this paper, we only take into account the casts where the misaligned sequences of words contain only one word or one multi-word unit in each sentence.


In order to assess the quality of our results, we calculated the similarity between all extracted pairs of synonyms following the distributional analysis paradigm as in (Moraliyski and Dias, 2007) who build context 9 feature vectors for noun synonyms.

we used the cosine similarity measure and the Loglike association measure (Dunning, 1993) as the weighting scheme of the context features.


In this paper we have introduced a new unsupervised methodology for synonym detection that involves extracting and aligning paraphrases on normalized domain corpora.

we have studied a specific structure within aligned paraphrases, paraphrase casts, from which valid synonyms were discovered.


A natural follow-up solution for discriminating between semantic types of extracted pairs could involve context-based classification of acquired casts pairs.

(Turney, 2008) tackled the problem of classifying different lexical information such as synonymy, antonymy, hypernymy and association by using context words.


We therefore cast the spoiler detection problem as a ranking problem so that comments that are more likely to be spoilers are to be ranked higher than others.


We have introduced the spoiler detection problem and proposed using topic models to rank movie comments according to the extent they reveal the movie's plot.

integrating linguistic cues from dependency information into our topic model significantly improves the ranking accuracy.


The sum (10-13) of the IDN, VHS, HS and MS document pairs found by the best full and selective fingerprint methods mentioned in the last paragraph is the same sum of the IDN, VHS, HS and MS document pairs found by the CA and CR methods.

the CA and CR are very close in their quailty to the best methods.


The meaning of this finding is that the abstracts in almost all the simple similar documents were not significantly changed.

the authors of the non-IDN documents did not invest enough to change their abstracts.


A very important finding is that the run time of FF was very expensive (one day, 3 hours and 57.3 minutes) compared to the run time of CA (9 hours and 16.7 minutes).

cA achieved almost the same results as FF but more efficiently.


Therefore, it and its equivalent reformulation by Hoyt and Baldridge (2008) in a multimodal variant of CCG are not safe (preserve all interpretations) and complete (remove all spurious ambiguities) for more commonly used variants of CCG.

this NF is not safe when the degree of composition is bounded, 1 and not complete when type-raising is a grammatical operation.


Since type-raising an argument Y to X/(X\Y) and applying it to the functor X\Y is semantically equivalent to applying X\Y directly to Y, type-raising is never required when function application can be used instead.

in all cases, a type-raised argument must be composed with another constituent, usually the original functor (head).


Some other effort is to mine bilingual corpora by textual means only.

two pieces of text are decided to be parallel merely from the linguistic perspective, without considering any hint from HTML markup or website structure.


On the contrary, it seems that the second problem can somewhat be alleviated, if we have a way to distinguish those terms that are likely to be correct translations from those terms that are not.

it may be worthwhile to reorder candidate TL keywords by our confidence in its translation quality.


In both tests the TF-IDFbased (Test 8) or the NE-based (Test 9) keywords are selected from only those TL words that appear both in the target page and the machine translated text of the source page.

we ensure that the keywords in the query must be correct translations.


The probability of the Viterbi alignment of a document pair is the sole basis on which we decide whether the pair is genuinely parallel.

an empirically determined threshold is used to distinguish parallel pairs from non-parallel ones.


Rather than relying solely on IE based or word co-occurrence features, this work adopts a holistic view of the different types of features useful for cross document coreference.

the main features of our proposed CDC approach are: • The proposed approach covers the entire spectrum of document level, sub-document context level and entity/relation level disambiguation evidence.


Specifically, the main features of our proposed CDC approach are: • The proposed approach covers the entire spectrum of document level, sub-document context level and entity/relation level disambiguation evidence.

we propose to use document categories as robust document level evidence.


Given a taxonomy T , we first formally define the affinity between a category c and one of its ancestor category c in T as: where len is the length of the shortest path between the two categories and depth(T) denotes the depth of the taxonomy.

affinity is the complementary of the normalized path length between c and its ancestor c .


Given two category lists, as the basis to measure similarity.

formally, we transform φ(d u ) to a K × K dimensional vector: where i, j = 1..K. we project φ(d u ) into a vector in the space spanned by the LCAs of category pairs.


We note that several existing state-of-the-art IE systems are also capable of extracting these features.

named Entity Recognition (nER) which focuses on a small set of predefined categories of named entities (e.g.


The text categorization component also contributes very important features.

tC3 is more significant than tC1 for reducing the Gini index because it recalls more correct categories.


improvement compared to CNTX in F (purity) and B-CUBED scores, respectively.

inverse purity improves by 46% relatively, implying that the additional evidence significantly improves the recall of coreferent entities (when there is a lack of context similarity in the traditional method).


Avg: We can further extend the idea of determining the strength of an edge by combining the extraction scores of both nodes connected by an edge.

where avg(u, v) is the average of the extraction scores assigned to the facts associated with nodes u and v. Nde: In addition to using extraction scores, we can also derive the strength of a node depending on the number of distinct relations it connects to.


However, to keep our evaluation manageable, we draw a random sample from these facts.

we first generate a ranked list using the extraction scores output by our extractor.


(In our experiments, we observed that 42% of the queries had at least one upper-case character.)

given a query Q = q 1 q 2 q 3 • • • q n , we define a candidate entity E = e 1 e 2 • • • e m as the maximal sequence of words (i.e., alpha-numeric characters) in the query such that each word e i in the entity begins with an uppercase character.


The set of candidate entities is then cleaned by applying a set of heuristics, thus producing the final set of entities.

for each extracted entity, we assign two confidence scores: a Web-based representation score and a query-log-based standalone score.


Our results suggest that query log spaces are more suitable to model the 'user space' wrt web features.

clickthrough information are most useful confirming our hypothesis that queries that generate clicks on the same urls capture similar user intents.


An adaptor grammar is a kind of "cache" model, in which previously generated subtrees are stored and more likely to be reused in later sentences.

while an adapted non-terminal A can expand to any tree rooted in A that can be constructed with the grammar rules, in practice it is increasingly likely to reuse the same trees over and over again.


However, most of them are not monotonic.

it can happen that we deduce an item I 2 from an item I 1 where the weight of I 2 is greater than the weight of I 1 .


The parser can therefore end up in a local maximum that is not the global maximum we are searching for.

our outside weights are only figures of merit (FOM).


They place their bets on manual cross-linking only and settle for the accuracy achieved thereof.

they do not explore the inclusion of small amount of annotated data from the target language to boost the accuracy (as mentioned earlier, supervised systems which use annotated data from the target language are known to perform better).


We thus estimate the value of P (h i |W, S) empirically from the Hindi sense marked corpus by making the following independence assumption: The rationale behind the above independence assumption becomes clear if we represent words and synsets using the Bayesian network of Figure 1.

here, the hindi word h i and the Marathi word W Figure 1: Bayesian network formed by a synset S and the constituent hindi and Marathi words are considered to be derived from the same parent concept S. they represent two different manifestations-one in hindi and one in Marathi-of the same synset S. Given the above representation, it is easy to see that given the parent synset S, the hindi word h i is independent of the Marathi word W .


Now we take up the other dimension of this economic system, viz., optimal use of annotated corpora for better accuracy.

if an application demands higher accuracy for WSD and is willing to pay for some annotation then there should be a way of ensuring best possible accuracy at lowest possible cost.


To simulate these methods, we adopted the distributional semantic similarity using web documents.

we computed the similarity between a keyphrase and its substring by cosine measure over collected the snippets from Yahoo!


Overall, we found that R-precision achieved the highest correlation with humans, above the interannotator correlation in all instances.

based on the evaluation methodology employed, it is performing slightly above the average level of a single annotator.


The decrease in average similarity suggests that if cosine similarity is used for the automatic generation of links then document pairs with higher value of l 1 .l 2 have a higher linked-pair likelihood than pairs with a smaller value of this quantity.

links created between documents with small l 1 .l 2 typically exhibit a larger value of semantic similarity than links created between documents with high value of l 1 .l 2 .


However, how many document pairs that could be linked in this similarity region have been missed by the users?

how much can our system help in the discovery of possible connections?


This paper advances the state-of-the-art performance in anaphoricity determination by ef-fectively capturing the structured syntactic information via a tree kernel-based method.

a dependency-driven scheme is proposed to dynamically determine the syntactic parse tree structure for tree kernel-based anaphoricity determination by exploiting constituent dependencies from both the syntactic and semantic perspectives to keep the necessary information in the parse tree as well as remove the noisy information.


(2008) and , we propose a new scheme to dynamically determine the syntactic parse tree structure for anaphoricity determination by exploiting constituent dependencies from both the syntactic and semantic perspectives to distinguish the necessary evidence from the unnecessary information in the syntactic parse tree.

constituent dependencies are explored from two aspects: syntactic dependencies and semantic dependencies.


For all of the 8 configurations evaluated, LL's recall, precision and MAP remain worse than LO's.

lO's MAP scores with the cosine measure are more than twice as high as ll's (respectively 0.33 and 0.124 for sentence contexts).


a set of content words without any word order or syntactic relation information.

the underlying assumption is that the sentimental orientation of the whole text depends on the sum of the sentimental polarities of content words.


frequency measurement and ratio measurement, where the first measures, the document frequency of a term in one category, and the second measures, the ratio between the document frequency in one category and other categories.

a novel method called Weighed Frequency and Odds (WFO) is proposed to incorporate both basic measurements: ) is thus to tune the weight between the two basic measurements.


In our approach, we first propose a machine-learning-based classifier to detect polarity shifting and then apply two classifier combination methods to perform polarity classification.

the polarity shifting training data is automatically generated through a feature selection method.


In this paper, we focus on negation scope finding instead.

we assume golden negation signal finding.


This paper explores negation scope finding from a parse tree perspective and formulates it as a shallow semantic parsing problem, which has been extensively studied in the past few years (Carreras and Màrquez, 2005).

the negation signal is recast as the predicate and the negation scope is recast as its arguments.


As a representative, Özgür and Radev (2009) divided speculation learning into two subtasks: speculation signal finding and speculation scope finding.

they formulated speculation signal finding as a classification problem while employing some heuristic rules from the parse tree perspective on speculation scope finding.


As far as negation scope finding considered, the negation signal can be regarded as the predicate 4 , while the scope of the negation signal can be mapped into several constituents which are negated and thus can be regarded as the arguments of the negation signal.

given a negation signal and its negation scope which covers word m , …, word n , we adopt the following two heuristic rules to map the negation scope of the negation signal into several constituents which can be deemed as its arguments in the given parse tree.


Table 2 shows the features in better capturing the details regarding the argument candidate and the negation signal.

we categorize the additional features into three groups according to their relationship with the argument candidate (AC, in short) and the given negation signal (NS, in short).


In addition, SVMLight 5 is selected as our classifier.

we adopt the linear kernel and the training parameter C is fine-tuned to 0.2.


We also report an oracle performance to explore the best possible performance of our system by assuming that our negation scope finder can always correctly determine whether a candidate is an argument or not.

if an ar-4) The oracle performance is less sensitive to automatic syntactic parsing.


In this paper we have presented a simplified shallow semantic parsing approach to negation scope finding by formulating it as a shallow semantic parsing problem, which has been extensively studied in the past few years.

we regard the negation signal as the predicate while mapping the negation scope into several constituents which are deemed as arguments of the negation signal.


Given a PCFG, the probability for a tree is the product of probabilities for the rules that it contains.

if a tree π is a context-free derivation that involves K rules of the form α k → β k , its probability is given by For example, the probability for the tree in Figure 2 is  Figure 3: Reconstructing original tree from virtual rules.


Therefore, we have to postpone the calculation of PCFG probabilities until reaching a natural non-terminal such as IP.

only when using the rule (1) to produce an item, the decoding algorithm can update PCFG probabilities because the original tree can be restored from the special node T now.


We still need to postpone the calculation of lexicalized PCFG probabilities until reaching a natural non-terminal such as IP.

only when using the rule (1) to produce an item, the decoding algorithm can update the lexicalized PCFG probabilities.


Note that the bilingual corpus does not contain the bilingual version of Penn Chinese Treebank.

all tree-to-string rules were learned from noisy parse trees and alignments.


Not surprisingly, our decoder is much slower than pattern matching on 1-best trees and packed forests (with the same beam size).

including Collins' Model 1 increases decoding time significantly because its sub-model probabilities requires back-off smoothing on the fly.


We assume here that the MT system is capable of providing word alignment (or equivalent) information during decoding, which is generally true for current statistical MT systems.

two types of semantic role features are proposed in this paper: a semantic role reordering feature designed to capture the skeletonlevel permutation, and a semantic role deletion fea-ture designed to penalize missing semantic roles in the target sentence.


Comparing these results with the ones obtained by our full featured approach, the application of roles improved especially the recall.

recall improved by 6% and 10% for recognition and classification respectively.


The obtained F β=1 results demonstrated that semantic roles are useful to recognize (81.43%) and classify (64.20%) TimeML events, improving the presented baseline by 19% for recognition and 30% for classification.

semantic roles employed as additional feature improved the recall of the non-roles version by 6% and 10% for recognition and classification respectively.


For this purpose, we propose to exploit structured ontology to obtain well-formed relevant aspects to a topic and use them to organize scattered opinions to generate a structured summary.

we focus on two main challenges in implementing this idea, (1) how to select the most useful aspects from a large number of aspects in the ontology and (2) how to order the selected aspects to optimize the readability of the structured summary.


Different from some existing work on exploiting ontologies, e.g., (Sauper and Barzilay, 2009), which relies on training data, we focus on exploring unsupervised approaches, which can be applied to a larger scope of topics.

given a topic with entries in an ontology and a collection of scattered online opinions about the topic, our goal is to generate a structured summary where representative major opinions are organized with well aligned aspects and in an order easy for human to follow.


Implementing the second and third steps involves special challenges.

without any training data, it is unclear how we should show the most interesting aspects in ontology with major opinions aligned and which presentation order of aspects is natural and intuitive for human.


To achieve this goal, our idea is to use human written articles on the topic to learn how to organize the aspects automatically.

we would order aspects so that the relative order of the sentences in all the aspects would be as consistent with their order in the original online discussions as possible.


Therefore, we resort to greedy algorithms to find approximations of the solution.

we view the problem as a ranking problem.


Our work addresses this shortcoming by proposing a morphologically sensitive approach to word alignment for language pairs involving a highly inflected language.

our method focuses on a set of closed-class morphemes (CCMs), modeling their influence on nearby words.


the alignments of closed-class morphemes (CCMs; later formally defined) as indicated by the dotted lines in (b) are overlooked in the IBM Model 4 alignment.


To find negative examples, we inventory other potential links that share the same lexical items with a positive one.

a link j -i not in U is a negative example, if a positive link j-i such that f j = f j and e i = e i exists.


w m -system: This baseline works at the word level just like the w-system, but differs at the alignment stage.

input to the IBM Model 4 training is the morpheme-level corpus, segmented by Morfessor and augmented with "+" to provide word-boundary information ( §4.1).


Natural languages allow us to express essentially the same underlying meaning as many alternative surface forms.

there are often many similar ways to say the same thing.


We are also considering adapting the system behavior to users.

we want to investigate dialogue control that adapts to the personality traits of users because it has been found that the flow of listening-oriented dialogues differs depending on the personality traits of users (Meguro et al., 2009b).


The above generation process defines the joint probability of source sentence s and its alignment a as follows: Our generative process provides the flexibility of incorporating different monolingual models into the probability distribution of a sentence pair.

we use the existing state-of-the-art nested Pitman-Yor n-gram language model as described by Mochihashi et al.


Rather than sampling the alignment or attempting to collapse it out (by summing over all possible alignments when calculating the relative probabilities of the merge and split hypotheses), we seek the best alignment for each hypothesis.

for each hypothesis, we perform a local search for a high-probability alignment of the merged word or split words, given the rest of alignment for the sentence.


Though syntactic parsers for English are reported to have accuracies over 90% on the Wall Street Journal (WSJ) section of the Penn Treebank (PTB) (McDonald et al., 2005;Sagae and Lavie, 2006;Huang, 2008;Carreras et al., 2008), broad-coverage parsing is still far from being a solved problem.

metrics like attachment score for dependency parsers (Buchholz and Marsi, 2006) and Parseval for constituency parsers (Black et al., 1991) suffer from being an average over a highly skewed distribution of different grammatical constructions.


This type of predictive models has been mostly studied in the context of embodied conversational agents (Nakano et al., 2003;Nakano et al., 2007).

backchannel feedback (the nods and paraverbals such as "uh-hu" and "mm-hmm" that listeners produce as someone is speaking) has received considerable interest due to its pervasiveness across languages and conversational contexts and this paper addresses the problem of how to predict and generate this important class of dyadic nonverbal behavior.


INDIVIDUAL EXPERTS Our first baseline model consists of a set of CRF chain models, each trained with different set of multimodel features (as described in the previous section).

only visual, prosodic, lexical or syntactic features are used to train a single CRF expert.


The lexical features are most important, but all feature classes are somewhat helpful.

including N-gram PMI features significantly improves the accuracy, from 94.0% to 95.4%.


Previous research has shown the importance of the citation summaries in understanding what a paper contributes.

(Elkiss et al., 2008) performed a large-scale study on citation summaries and their importance.


To measure how randomly a set of consecutive terms are forming an N -gram, Tomokiyo and Hurst (Tomokiyo and Hurst, 2003) use pointwise divergence.

for an N -gram of size i, This equation shows the extent to which the terms forming g i have occurred together randomly.


In particular, for an N -gram of size i, This equation shows the extent to which the terms forming g i have occurred together randomly.

it indicates the extent of information that we lose by assuming independence of each word by applying the unigram model, instead of the N -gram model.


We set the criteria of choosing a sequence of words as significant to be whether it has positive pointwise divergence with respect to both the background model, and individual terms of the foreground model.

we extract all g i from FM for which the both properties are positive: The equality condition in the second equation is specifically set to handle unigrams, in whicĥ .


Finally, MMR uses the full cosine similarity matrix and greedily chooses sentences that are the least similar to those already selected for the summary (Carbonell and Goldstein, 1998).

where A is the set of sentences in the summary, initially set to A = ∅.


Given the availability of this semantic subtype-annotated corpus, we adopt a supervised machine learning approach to semantic subtype determination.

given (the boundary of) an NP, we train a classification model to determine which of the 92 semantic subtypes it belongs to.


Three features are derived from the string of NP i .


Finally, if NP i is a nominal, we create a feature based on its WordNet equivalent concept.

for each entity type defined in ACE 2005 5 , we create a list containing all the word-sense pairs in WordNet (i.e., synsets) whose glosses are compatible with that entity type.


One weakness of the baseline classification model is that it classifies each instance independently.

the model cannot take into account relationships between them that may be helpful for improving classification accuracy.


We assume that g can be decomposed into a product of m factors.

where each factor f k is a real-valued function of some subset of X 1 , ... , X n , denoted as s k (X 1 , ..., X n ).


Next, we exploit the relationship between two random variables.

we want to encourage the inference algorithm to assign the same label to two variables if there exists a relation between the corresponding NPs that can provide strong evidence that they should receive the same label.


This model is trained in essentially the same way as the baseline model described in Section 3.

it is trained on the same set of instances using the same feature set as the baseline model.


After training, we can apply the supertype classification model and the supertype-specific subtype classification model to determine the semantic subtype of an NP in a hierarchical fashion.

we first employ the supertype model to determine its semantic supertype.


We examined the under-studied problem of semantic subtype induction, which involves classifying an NP as one of 92 semantic classes, and showed that two techniques -hierarchical classification and collective classificationcan significantly improve a baseline classification model trained using an off-the-shelf learning algorithm on the BBN Entity Type Corpus.

collective classification addresses a major weakness of the standard feature-based learning paradigm, where a classification model classifies each instance independently, failing to capture the relationships among subsets of instances that might improve classification accuracy.


In this paper we bring active learning to life in the context of frame semantic annotation of German texts within the SALSA project (Burchardt et al., 2006).

we apply AL methods for learning to assign semantic frames to predicates, following Erk (2005) in treating frame assignment as a Word Sense Disambiguation task.


We will investigate increasingly refined models that are tailored to the nature of our task and underlying representations.

we extend their approach by exploiting semantic markup in the NL direction corpus.


Our near-term goal is the integration of the morphology learning algorithms into the language-independent Text-to-Speech (TTS) system for improvement of grapheme-to-phoneme rules, stress prediction and tone assignment.

the morphology learning algorithms described in this paper will be incorporated into the available isiZulu TTS system for automatic prediction of lexical tones.


The stopping criterion is to prevent overgeneralisation.

the algorithm needs to be stopped before getting the initial Mcorpus (since no merging is possible).


As a fundamental and effective tool for document understanding and organization, multidocument summarization enables better information services by creating concise and informative reports for a large collection of documents.

in multi-document summarization, given a set of documents as input, the goal is to produce a condensation (i.e., a generated summary) of the content of the entire input set (Jurafsky and Martin, 2008).


Then we define the mean normalized frequency (MNF) of a seed set S, as follows, where F norm (t) is the normalized frequency of t. The MNF values for the five seed sets are listed in Table 3, where we can see that proper nouns have the largest MNF values, followed by common nouns.

the patterns in Table 2 capture the relations of more proper nouns than other term categories.


We propose three methods to discover the subsumption relation between tags.

the tagged document's content is used to find the relations, which leads to better result.


Third, it is hard to evaluate the correctness of clustering.

it is hard to tell if two tags are similar or not.


The most intuitive way to estimate p(t a |t b ) is via tag-tag co-occurrences.

we use the following formula: where is the number of documents that are annotated by both t a and t b , and is the number of documents annotated by t b .


Given a set of subsumption relations, multiple Layered-DAGs may be constructed.

we want to find the Layered-DAG that maximizes the sum of all edges' weights.


In this section, we show the experimental results of proposed methods.

we focus on the following points: • The quality of discovered subsumption relations by different methods.


While accounting for these computational requirements, a morphology metric should still reward accurate models of linguistic structure.


Multi-party online conversations are particularly interesting because they become a pervasive form of communication within virtual communities, ubiquitous across all age groups.

a great amount of communication online occurs in virtual chat-rooms, typically conducted using a highly informal text dialect.


To address the above issues, we present a statistical framework based on Maximum Entropy (MaxEnt) model.

we consider subtree alignment as a binary classification problem and use Maximum Entropy model to classify each instance as aligned or unaligned.


To be fully consistent with the definition, we strictly preserve the semantic equivalence for the aligned sub-trees to keep a high precision.


For rare noun phrases, however, the MT may produce erroneous word order that can lead to serious distortions in the meaning.

difficult are nominal phrases from specialized domains that involve challenging terminology, for example: "group reference attribute" and "validation checking code".


For a given product, technology, or industry sector we identify and aggregate statements on its market development found on relevant websites.

we extract time information ("by 2015") and money information ("$9B") and use support vector classification to identify sentences that represent market statements.


For multi-document summarization, the proposed UnifiedRank approach outperforms all the three graph-based baselines over all three metrics on the DUC2001 dataset, and it outperforms the three baselines over ROUGE-1 and ROUGE-W on the DUC2002 dataset.

unifiedRank can significantly outperform BasicRank and TwoStageRank over all three metrics on the DuC2001 dataset (t-test, p-value<0.05).


In this paper, we extensively explore characteristics of questions in cQA, and propose a fully automated approach to detecting question sentences.

we complement lexical patterns with syntactic patterns, and use them as features to train a classification model that is capable of handling informal online languages.


The reason is that if certain irregular forms frequently occur on the Web, there will be statistical evidences that the syntactic patterns derived from it, though not desired, will commonly occur as well.

we take the wrong patterns and utilize them to detect questions in the irregular forms.


In this section, we present empirical evaluation results to assess the effectiveness of our question detection model.

we first examine the effects of the number of patterns on question detection performance.


Our approach exploits salient question patterns at both the lexical and syntactic levels for question detection.

we employ the one-class SVM algorithm such that the learning process is weakly supervised and no human annotation is involved.


Depending on the specific application domain, more sophisticated and verbose tree edit operations can be designed and easily incorporated into our model.

tree edit operations involving deleting, inserting or substituting entire treelets seem interesting and promising, requiring merely a simple extension to the forwardbackward dynamic programming.


The intuitive idea is that during training, we want to maximize the weights of the positive examples in the positive state set and minimize their weights in the negative state set, and vice versa.

we want the positive state set to attract positive examples but push away negative examples.


On RTE3, our model outperforms the other syntax-based systems compared.

out system gives the same precision level as the second best system (de Marneffe et al., 2006) without sacrificing as much recall, which is the most common drawback found in syntax-based systems.


To assure that, when a span is not tokenized into a single word by the extracted rules, we will add an operation, which is considering the entire span as an OOV.

we tokenize the entire span into a single word with a translation that is the copy of source side.


the model only needs to include 10 predictors to achieve an increase of around 39% (93.6-67)/67) in model accuracy.


A closer look at these sentences suggests that they are strongly biased toward postver-bal word order, in that 400 out of 428 (95.4%) tigong sentences are postverbal (compared with the average level of 67% in all sentences).

just by guessing postverbal every time, one is able to make the correct prediction for tigong over 95% of the time.


Similarly, the accuracy at Rank 2 can be computed by considering the top two near-synonyms proposed by each classifier.

if either the near-synonym with the highest or the second highest score was the correct answer, the test sample was considered correctly classified.


municipal receive duty octopus eight foot As the examples suggest, we are able to build a set N E with features similar to the features of N .

each target word is paired with a related word sequence derived from its etymology.


In the rest of the section, we firstly give the experimental setup and then we discuss the experiments and the results.

our experiments aim to compare compositional distributional semantic (CDS) models with respect to their ability of detecting statistically significant difference between sets E and N E. the average similarity sim( z, (xy)) for ( z, x, y) ∈ E should be significantly different from sim( z, (xy)) for ( z, x, y) ∈ N E. In this section, we describe the chosen similarity measure sim, statistical significance testing and construction details for the training and testing set.


In this work, we use the Expectation-Maximization (EM) algorithm (Dempster et al., 1977).

we use the naïve Bayesian EM formulation in (Nigam et al., 2000), which runs a Bayesian classifier iteratively on the labeled and unlabeled data until the probabilities for the unlabeled data converge.


However, in the subsequent iterations, SL is treated in the same way as any examples in U.

the classifier f x from each iteration x (including f 0 ) will predict U.


There are conflict situations that need to be resolved.

u may satisfy a constraint of more than one labeled sub-set L i .


The class j with the highest score is assigned to u.


It shows whether the constraints alone are sufficient to produce good results.

the final result is the expanded seeds SL plus the rest of U assigned randomly to different groups.


Beam size plays an important role for the forest-guided supertagger training method, since a larger beam size reduces the possibility of search errors.

speaking, we control the beam size by limiting the number of edges in the chart in both the forest-guided supertagger training process and the final parsing.


From the results shown in Figure 2, we observe that our method outperforms all the top 5 systems and the baseline system of KBP-09.

our method achieves better result than both similarity ranking approaches.


Since the best M and δ for the MUC evaluation metric were not known, we used held-out development sets to tune the parameters.

we trained classifiers with different combinations of M and δ on a development training set, and evaluated their performances on a development test set.


1 www.baidu.com This paper proposes a method that uses search engine query logs for extracting paraphrases, which is illustrated in Figure 1.

three kinds of paraphrases can be extracted with our method, which include (1) query-title (Q-T): a query and a document title that users clicked on; (2) query-query (Q-Q): two queries, for which users clicked on the same document title; (3) titletitle (T-T): two titles that users clicked on for the same query.


However, in consideration of precision, we extract them from paraphrase Q-T. We call our paraphrase Q-Q and T-T extraction approach as a pivot approach, since we use titles as pivots (queries as targets) when extracting paraphrase Q-Q and use queries as pivots (titles as targets) when extracting paraphrase T-T. Our paraphrase extraction algorithm is shown in Table 2.

lines 1∼7 extract para-phrase Q-T from the query log.


• Pivot Fertility Feature F P F : F P F is a feature specially designed for paraphrase Q-Q and T-T extraction, which are based on the pivot approach 4 .

we define fertility of a pivot as the number of targets it corresponds to.


Hence we will improve the method in our following experiments.

multiple slots will be allowed in a pair of patterns.


Our contribution is that we, for the first time, propose to extract paraphrases from user queries and the corresponding clicked document titles.

three kinds of paraphrases are extracted, which can be (1) a query and a hit title, (2) two queries that hit the same title, and (3) two titles hit by the same query.


We have 6 raters in the evaluation, all of whom are postgraduate students.

3 raters major in English, while the other 3 major in computer science.


On the whole, the paraphrase rates of the approaches are not high.

we can see that the paraphrase rate of D-2 is clearly higher than D-1, which is in line with our intention of designing D-2.


In our experiments, we find that for about 90% test sentences, the paraphrases generated by the decoding-based approach D-1 are outside the candidates.

we compare the paraphrases generated by S-1 and D-1 and find that, for about 40% test sentences, S-1 gets higher scores than D-1, while for another 21% test sentences, D-1 gets higher scores than S-1 8 .


In this paper we focus on the problem of resolving references of named-entities and concepts in natural language through their textual surface forms.

we present a method of resolving surface forms in general text documents to Wikipedia entries.


Despite the distinctions in tag sets and bracket structures, these parse trees actually have partial agreements in bracket structures.

not all bracket structures in the parse trees are different.


That is, not all bracket structures in the parse trees are different.

put, although the internal structures of the parse trees are different, both CTB and TCT agree to take "中国 传统 文化" as a noun phrase.


As already mentioned above, we apply Collins parser in this paper.

speaking, two CKY-style baseline decoders to participate codecoding are built on CTB and TCT respectively with Collins model two.


Then all candidates with the same loc and l are merged to one by weighted summing their probabilities.

suppose that there are n system outputs to be combined, with the i-th output's weight to be w i .


Future work will focus on further evaluation on a range of different clustering algorithms in order to find an optimal approach.

we believe that using overlapping or fuzzy clustering algorithms may counterbalance problems of entity pair ambiguities.


The Arabic article is not handled well by a BOW representation for similar reasons as for Bouazizi.

it contains poems about negative phenomena like mudslinging and sadness; and it describes the negative behavior of fellow poets towards Moutanabbi -this is negative, but will not create a negative impression of Moutanabbi in the mind of the reader.


Second, we pointed out that there seem to be cultural differences in the magnitude of absolute POV.

we found that international personalities are viewed in more positive light in the Arabic Wikipedia than in English.


During the evaluation we also observed over-specification, where the translation into German Die Forderungen ..., 24 does not require the German article die at the beginning.

to the German language we further observed some compound errors, e.g.


A formal evaluation requires measuring the semantic distances of extracted semantics against the benchmark semantics.

the spans of texts need to be mapped to concepts and their relationships in the reference semantic structure, which provides a consistent and formal representation of biomedicine.


As the approach focuses at the semantic level (concept), it can be applied on languages included in the Unified Medical Language System (UMLS).

it can be applied for those languages in the Unified Medical Language System's knowledge sources (e.g.


Semantic triples can be constructed at the level of semantic atoms, concepts, types and groups.

semantic types in each semantic group, semantic concepts in each semantic type, and semantic atoms in each semantic concept are extracted to have explicit representation of the structure.


types Remove the ancestor STs, if any Hierarchical relationship inconsistencies occur in cases where T 1 becomes an ancestor of T 2 in relationship conditions if C 1 and C 2 are related taxonomically in MT (C 1 isa C 2 ), and C 1 is in T 1 (C 1 isa T 1 ), C 2 is in T 2 (C 2 isa T 2 ).

t 1 must be decedent or the same as t 2 to make consistent.


In this article, the semantic mapping considers only semantic relationships in MT indicated in table 1.

for example, other relationships (RO) and unspecified relationships (RU) are considered for associative semantics mapping.


While the translation quality based data selection technique performs well in the experiments presented in this paper, there is a risk that the approach may overfit on the small devsets used (small devsets are a typical situation in real-life domain adaptation scenarios).

this can happen if the set is not 'fully' representative of the target domain in question.


A co-training approach is taken, with two classifiers, one designed to model the contexts surrounding occurrences of paraphrases, and the other trained to identify significant features of the words within paraphrases.

we use morpho-syntactic features calculated for both classifiers, as is to be expected when working with highly inflected languages.


Our assumption is that this subset is the one with highest diversity with respect to languages among OL.

while most of the outlinks in OL point to pages in similar languages, the links in EP point to pages in distinct languages.


The entries in the dictionary include stem forms, alternate forms, glosses in English and Nepali, as well as some grammatical information.

the dictionary lists coarse-grained part of speech (drawn from a set of 30 tags) as well as fairly detailed information about the syntactic and semantic valence of verbs, that is, the number of arguments expected, the cases for each argument, and an indication of which argument(s) the verb agrees with.


Here, the above-mentioned domain-specific phenomena are of a semantic kind.

we show that two specific phenomena, polysemy and synonymy, are reflected by the counts of two motifs, the chain motif and the box motif (#2 and #4 in Fig.


In this work, we explore the possibility of using non-lexical and thread specific features for the subjectivity classification of threads.

we explore the following research question: Can non-lexical thread specific features (e.g., number of users in a thread, number of posts in a thread, etc.)


With no restriction on what an actual word may look like, high-frequency patterns of any kind -including individual segments and short n-gram like sequences of segments -can be employed by the models to explain the input they get.

for little input with overall few word tokens and, consequently, relatively few repetitions for each of the actual word types, the evidence for high-frequency non-words is extremely high, leading to over-rather than undersegmentation.


We expect the identity and number of modifier components to be strongly correlated with the identity of the head.

the conditional distributions of modifier given head will be sharply peaked.


Linking Elements In the preceding definition of compound segmentation, the linking elements do not constitute items in the vocabulary .

regarding linking elements as components in their own right would sacrifice important contextual information and disrupt the conditionals faced with the compound Küche•n•tisch, we want P(küche|tisch) in the model, but not P(küche|n).


(2011) examined how individuals change their language usage depending on the status of the individual with whom they are communicating.

they examine the use of upspeak and downspeak for signaling power relationships where upspeak is a sign that an individual is communicating with someone of higher status and downspeak is a sign that an individual is communicating with someone of lesser power.


Gibbs sampling is a form of Markov Chain Monte Carlo (MCMC), which is used to obtain a sequence of random samples from a joint probability distribution of two or more random variables.

gibbs sampling is used when direct sampling is prohibitive.


After discovering gappy patterns using the described generative model, we build a binary classifier for each of the social acts we wish to identify.

we use a logistic regression classifier to the learn a set of weights for each of the gappy patterns, which denotes the discriminatory ability of the pattern in identifying the social act.


It is unclear to what extent previous results on this task are influenced by these effects, but we believe there is at least reason to be skeptical of some of the conclusions.

sophisticated feature selection techniques which have been the focus of recent work may result in models which perform better in the ICLE, but which have little or no benefit beyond that particular corpus.


The idea was to take advantage of statistical methods and proceed per standard methods currently embraced in the field.

use an available corpus that should in principle be large enough to adequately reflect language use and to extract bigrams from this corpus in order to identify patterns in N+V combinations and to use our knowledge of the extracted patterns in further NLP applications.


One type, Class B in Table 1, does not allow the subject to be non-agentive.

it does not allow combinations with those light verbs that require a dative subject, cf.


The goal of our experiment was to expand the search space by using automatized methods, to thus extract information about a significant number of nouns and to be able to confirm, expand or revise Ahmed and Butt's proposal.

understanding the semantic constraints on N+V CP formation in more detail would be welcome for further NLP applications.


we combined the information of the four lists into one list recording the light verb behavior of every single word.

we had different classes of words: words occurring with all four light verbs or words with only kAr 'do' and ho 'be', hu-'become' and rAk h 'put' or words occurring with various combinations of these verbs.


With respect to verb conjugation, we also naturally normalized over the bigrams we looked for.

we looked for a total of 238 different forms of our set of four verbs, but normalized the different inflected versions to just the stem form for purposes of bigram storage.


Manual inspection of some of the CPs we found showed that not all possible combinations of N/ADJ/P+V were in fact attested in the corpus.

our bigram lists will need to be complemented by manual native speaker work (traditional lexicography).


This experimentation was successful as it allows for quick visual analysis of our data sets, which in turn also enables a non-labor-intensive way of further cleaning and culling our data.

the visual analysis allowed us to be able to quickly assimilate information about complex interrelationships in our data: which types of verbs does the word in question occur with with what level of frequency and how does this compare to other words in the list?


Thus for each linked mention m di we have its result tuple, R(m d i ) which consists of a linker score and a list of k targets, ordered according to their ranker score.

we abbreviate the first and second elements of  for any surface string, we consider all target concepts and associated ranker scores, and associate the string with the highest linker score of any matching mention.


For example, we assume the adversary is a human acting passively rather than actively.

we have ignored the possibility of computational steganalysis and steganographic attacks, such as detecting, extracting and destroying the hidden message (Fridrich, 2009).


5 A pleonasm consists of two concepts (usually two words) that are mutually redundant: examples are free gift, cold ice or final end.

pleonasms contain unnecessary words, and those words can be removed without affecting the meaning of the text.


Note that we only care about the naturalness of the resulting sentence rather than the meaning retention of the original sentence.

the evaluation is based on the sentence-level naturalness rather than the coherence of the whole document.


For linguistic steganography, there exists a convenient modularity between the linguistic transformation and the embedding method.

the utility of a specific embedding method does not imply a particular linguistic transformation, although it will put some constraints on what transformation can be used.


According to our observations from section A of the BNC, on average there are two deletable adjectives per five sentences.

the payload upper bound of using the adjective deletion technique is around 0.4 bits per sentence if a deletion encodes a secret bit.


Linguistic steganography is a form of covert communication using natural language to conceal the existence of the hidden message so that the very act of communication is undetectable to an outside observer (human or computer) (Fridrich, 2009).

the covert communication would fail if an outside observer is suspicious of the existence of the hidden message.


a secret embedding method can treat the linguistic transformation as a black box for outputting alternatives for a given cover text.


Our goal in this paper is to improve the state of the art in Chinese event extraction.


and apply learningbased pruning to further prune the candidate triggers.

we train a classifier on the training data to determine whether a candidate is a trigger or not.


But since never appears in the training set, the word feature is useless as far as classification is concerned.

although and are similar verbs, the word feature does not capture such similarity, and therefore the classifier cannot exploit this similarity when classifying .


As a result, we propose to employ features that encode semantic role information for trigger identification and trigger type classification.

we introduce five features: whether the word under consideration is a predicate according to the semantic role labeler; if yes, the entity type and subtype of its Arg0; the entity type and subtype of its Arg1.


In our experiments, we apply a rule-based method for zero pronoun resolution.

to detect zero pronouns, we employ a simple heuristic, which posits that a zero pronoun exists before a word if it is a verb and it has no overt subject in the corresponding syntactic parse tree.


12 Here is the reason behind using roles as feature values: some roles are more important than the others.

roles for arguments that serve as subjects or objects, such as Victim, are intuitively more important than roles of adjunct arguments, such as Place.


Feature selection is done using cross validation on the training documents.

we partition the training documents into 9 folds, train the classifier whose features are to be selected on 8 folds, apply the classifier to the remaining fold, and repeat this process 9 times in order to select the feature groups that have the best average performance over the 9 folds when used in combination with the baseline features.


Recall that the DC features were not selected by the feature selection algorithm for our best-performing system, so none of the results in this table involves DC features.

we start with the baseline features without any DC features (row 1), and add the feature groups incrementally to this baseline feature set.


Our results also indicated that all but the argument consistency feature contributed positively to overall performance.

while each of these feature groups provided small gains, their cumulative benefits were substantial.


It serves as the back-off model during the runtime decoding.

moses searches the backoff model only when no translation for a phrase is found in the background model.


Our algorithm alternates between performing an E-step and M-step.

it first trains a classifier based on the available labeled messages as well as some auxiliary cues mined from the web, and probabilistically predicts the class labels of the unlabeled messages.